End-to-end integrity verification is used to avoid silent data corruption in file transfers by comparing the checksum of files at source and destination end points. However, it increases transfer times significantly as checksum computation requires reading files back from the storage and running compute-intensive hash computation. In this paper, we propose Fast Integrity VERification (FIVER) algorithm

The demand for computing power, along with the diversity of computational problems, culminated in a variety of heterogeneous architectures. Among them, hybrid architectures combine different specialized hardware into a single chip, comprising a System-on-Chip (SoC). Since these architectures usually have limited resources, efficiently splitting data and tasks between the different hardware is primal

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) bring unprecedented performance requirements for automotive systems. Graphic Processing Unit (GPU) based platforms have been deployed with the aim of meeting these requirements, being NVIDIA Jetson TX2 and its high-performance successor, NVIDIA AGX Xavier, relevant representatives. However, to what extent high-performance GPU configurations

The problem of using cloud computing resources for services is related to planning the amount of resources needed and their subsequent reservation. This problem occurs both on the side of the customer who tries to minimize the cost of the service and on the side of the cloud provider who wants to make the best use of existing infrastructure without introducing any modifications. In our article, we

Blockchain is a relative recent research and technological trend, with applications in diverse domains including those associated with a nation’s critical infrastructure sectors (e.g., chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, and energy). The interest in blockchain is also partly evidenced by the number of submissions

Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data

Hyperspectral image registration is a relevant task for real-time applications such as environmental disaster management or search and rescue scenarios. The HYFMGPU algorithm was proposed as a single-GPU high-performance solution, but the need for a distributed version has arisen due to the continuous evolution of sensors that generate images with finer spatial and spectral resolutions. In a previous

The over operator is commonly used for α-blending in various visualization techniques. In the current form, it is a binary operator and must strictly follow a specific composition order of all participating operands, hence posing a significant performance limit. In this paper, we derive a set of generic formulas for the over operator that work with any number of operands and completely remove the restriction

Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input. However, few works focus on fully utilizing GPU resources when the input is not regular-shaped. Current optimizations do not consider fully utilizing the memory bandwidth and computing power; therefore

Distributed Denial-of-Service attacks have been a challenge to cyberspace, as the attackers send a large number of attack packets similar to the normal traffic, to throttle legitimate flows. These attacks intentionally disrupt the services offered by the systems resulting in heavy cost. A flash crowd or flash event is an unexpected surge in the number of visitors to a particular website resulting in

Advanced persistent threats (APTs) have become a major cyber threat to large organizations. To steal confidential data from specific organizations, attackers adopt highly targeted intrusion schemes. Prior to stealing critical data, APT activities hide themselves in legitimate activities and consistently elevate their privileges, making them very difficult to detect. The detection of malicious domains

Energy predictive modeling is the leading method for determining the energy consumption of an application. Performance monitoring counters (PMCs) and resource utilizations have been the principal source of model variables primarily due to their high positive correlation with energy consumption. Performance events, however, have come to dominate the landscape due to their better prediction accuracy

Parallel/Distributed particle filters have been widely used in the estimation of states of dynamic systems by using multiple processing units (PUs). In parallel/distributed particle filters, the centralized resampling needs a central unit (CU) to serve as a hub to execute the global resampling. The centralized scheme is the main obstacle for the improved performance due to its global nature. To reduce

Recently, a very deep convolutional neural network (CNN) has achieved impressive results in image super-resolution (SR). In particular, residual learning techniques are widely used. However, the previously proposed residual block can only extract one single-level semantic feature maps of one single receptive field. Therefore, it is necessary to stack the residual blocks to extract higher-level semantic

For data-parallel frameworks, their communication is highly structured. Coflow is a networking abstraction proposed for their all-or-nothing job-specific semantics. Minimizing coflow completion time (CCT) decreases the completion time of corresponding jobs. However, state-of-the-art coflow scheduling approaches suffer from several drawbacks. On the one hand, both sender-driven and receiver-driven scheduling

Many server applications suffer from a bottleneck in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is hard for developers and runtime systems to ensure that all critical resources are fully exploited by a single application, so an attractive technique for increasing server system utilization is to colocate multiple

In recent years, the areas of High-Performance Computing (HPC) and massive data processing (also know as Big Data) have been in a convergence course, since they tend to be deployed on similar hardware. HPC systems have historically performed well in regular, matrix-based computations; on the other hand, Big Data problems have often excelled in fine-grained, data parallel workloads. While HPC programming

Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and their properties and structure vary over time. These are seen in social, telecom, transit and epidemic networks. However, current graph databases and query

In this paper, we have addressed a speed-area efficient VLSI implementation of a cellular automaton (CA) based random number generator (RNG) on Field Programmable Gate Arrays (FPGAs), in which each CA cell was proposed to be a multi-bit word in the original algorithm. This is in contrast to typical CA algorithms comprising one bit per CA cell. The original algorithm is shown favorable for FPGA implementations

In the mesh-connected processors, some processor elements (PEs) become ineffective due to high temperature, overload and other factors, which can affect the stability of the system. This paper deals with the problem of reconfiguring the largest possible subarray from the processor with faults under the row and column rerouting constraint. Firstly, a flexible routing scheme, based on dynamic programming

Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and scalable hash trie map design that fully supports the concurrent search, insert and remove operations on hash maps. To the best of our knowledge, our proposal is the first that puts together the following characteristics: (i) be lock-free; (ii) use

With the advent of Internet of Things (IoT) connecting billions of mobile and stationary devices to serve real-time applications, cloud computing paradigms face some significant challenges such as high latency and jitter, non-supportive location-awareness and mobility, and non-adaptive communication types. To address these challenges, edge computing paradigms, namely Fog Computing (FC), Mobile Edge

Hypercube (HC) networks (N=2n) provide efficient communication for parallel-and-distributed computing (PDC) but the HC-based multi-processor (MP) system is costly and not scalable, while hierarchical hypercube (HHC) networks (N=2n, n=2m+m) are less expensive and more scalable. However, the traditional HHC-routing easily conflicts, especially when executing multiple tasks (k > 2m nodes-per-task). In

A Peer-to-Peer (P2P) network is a dynamic collection of nodes that connect with each other via virtual overlay links built upon an underlying network (usually, the Internet). P2P networks are highly dynamic and can experience very heavy churn, i.e., a large number of nodes join/leave the network continuously. Thus, building and maintaining a stable overlay network is an important problem that has been

Renaming is a task in distributed computing where n processes are assigned new names from a name space of size m. The problem is called tight if m=n, and loose if m>n. In recent years renaming came to the fore again and new algorithms were developed. For tight renaming in asynchronous shared memory systems, Alistarh et al. describe a construction based on the AKS network that assigns all names within

Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference algorithm maps the observation data into a binary value which represents either x causes y or y causes x. The nature of these algorithms makes the results unstable with the change of data points. Therefore the accuracy of

Information Centric Networking (ICN) provides caching strategies to improve network performance based on consumer demands from the intermediate routers. It reduces the load on content server, network traffic, and improves end-to-end delay. The content requesters use an Interest packet containing the name of data to express their needs. If such Interest packets are routed efficiently, the end to end

In the cryptocurrency literature, Proof-of-Space has been a potential alternative for permissionless distributed consensus protocols not only due to its recyclable nature but also the potential to support multiple chains simultaneously. Namely, the same storage resource can be contributed to the consensus of more than one chain. However, a direct shared proof of the same storage brings about newborn

Task offloading strategy optimization in mobile edge computing (MEC) has always been a hot issue. However, the mobility of a user equipment (UE) seriously affects the UE’s cost and performance. This paper proposes three mobility types depending on whether the mobility characteristic of a UE is known, and formulates an energy minimization problem and a latency minimization problem to optimize the cost

The recent advancement in the field of electronics has led to development of sensors that capture the image of an area or object in spectral-domain along with spatial information. Due to continuity of spectral domain in hyperspectral images, it is difficult to store, process, analyze or transmit the critical information contained in it. Prediction based compression technique is used to reduce this

Dengue is one of the most common and widespread infectious illnesses in humans transmitted by female Aedes albopictis. The prevalence of Dengue cases has increased substantially leading to human morbidity. Inadequate availability of healthcare professionals and inaccessibility to healthcare institutions have aggravated the problem. The traditional medical technologies are too antiquated to serve the

The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application’s

In recent years, there has been growing interest in using metaheuristic algorithms to solve various complex engineering optimization problems. Most of the real-world problems comprise of more than one objective. Due to the inherent difficulty of such problems and lack of proficiency, researchers in different domains often aggregate multiple objectives and use single-objective optimization algorithms

Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main challenges: privacy and security risks, information silo, and pricing mechanisms and incentives. The recent advances of blockchain bring opportunities to address

Wireless sensor network barrier coverage plays an important role in intrusion detection. How to construct a robust barrier is a key research issue. For the initial deployment of barriers, with the depletion of node energy, some node dies prematurely, resulting in the existence of more weak points in the barrier. A method of using the re-deployment of mobile nodes to strengthen the barrier is proposed

Network coding and topology control techniques have been widely used to increase throughput and improve the lifetime of Wireless Sensor Networks (WSNs). This paper considers the simultaneous utilization of these techniques in a WSN and proposes convex non-linear programming. Since solving the problem for a large-scale and dynamic WSN is impractical and almost impossible, Lagrangian, sub-gradient and

In large-scale data-parallel analytics, shuffle, namely the cross-network read and the aggregation of partitioned data between tasks with data dependencies, usually bring in large overhead. To reduce shuffle overhead, we present SCache, an open-source plug-in system that particularly focuses on shuffle optimization. SCache adopts heuristic pre-scheduling combining with shuffle size prediction to pre-fetch

3D image registration is one of the most fundamental and computationally expensive operations in medical image analysis. Here, we present a mixed-precision, Gauss–Newton–Krylov solver for diffeomorphic registration of two images. Our work extends the publicly available CLAIRE library to GPU architectures. Despite the importance of image registration, only a few implementations of large deformation

Cloud computing is a computing method based on the Internet designed to share resources through virtualization technology. For a large number of requests waiting to be processed, task scheduling is used to reasonably allocate computing resources to requests. With the rapid development of computer hardware and software, deep reinforcement learning (DRL) provides a new direction for better solving task

The recent advances in the cyber–physical domains, cloud and edge platforms along with the advanced communication technologies play a crucial role in connecting the globe more than ever, which is creating large volumes of data at astonishing rates and a tsunami of computation within hyper-connectivity. Data analytic tools are evolving rapidly to harvest these explosive increasing data volumes. Deriving

K Nearest Neighbor (KNN) joins are used in scientific domains for data analysis, and are building blocks of several well-known algorithms. KNN-joins find the KNN of all points in a dataset. This paper focuses on a hybrid CPU/GPU approach for low-dimensional KNN-joins, where the GPU may not yield substantial performance gains over parallel CPU algorithms. We utilize a work queue that prioritizes computing

The robots using visual simultaneous localization and mapping (SLAM) system are generally experiencing excessive power consumption and suffer from depletion of battery energy during the course of working. The intensive computation necessary to complete complicated tasks is overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy

With the rise of concerns over security and privacy in the cloud, the “security-on-demand” service mode dynamically provides cloud customers with trusted computing environments according to their specific security needs. Major challenges, however, remain to achieve this goal: (1) integrating an auditable, tamper-resistant trust-management mechanism into the cloud infrastructure and (2) building a protocol

With the development of blockchain-based digital currencies, the security of digital wallets becomes more and more important. As far as we know, there is no safe lightweight wallet in hyperledger fabric. To solve the problem, we proposed a Trustzone-based Secure Lightweight Wallet for Hyperledger Fabric (hereafter referred to as TSLWHF). Firstly, we designed an Unspent Transaction Output (UTXO) set

Recent trends in high-performance computing and deep learning have led to the proliferation of studies on large-scale deep neural network training. However, the frequent communication requirements among computation nodes drastically slow the overall training speeds, which causes bottlenecks in distributed training, particularly in clusters with limited network bandwidths. To mitigate the drawbacks

Spark is a more efficient distributed big data processing framework following Hadoop. It provides users with more than 180 adjustable configuration parameters, and how to choose the optimal configuration automatically to make the Spark application run effectively is challenging. The key to address the above challenge is having the ability to predict the performance of Spark applications in different

A fundamental layer of smart cities, the Internet of Vehicles (IoV) can significantly improve transportation efficiency, reduce energy consumption, and traffic accidents. However, because of the vehicle and the RoadSide Units (RSU) use wireless channels for communication, the risk of information being leaked or tampered is highly increased. Therefore, secure and reliable authentication and key agreement

As a distributed hash table (DHT) routing overlay, Skip Graph is used in a variety of peer-to-peer (P2P) systems including cloud storage. The overlay connectivity of P2P systems is negatively affected by the arrivals and departures of nodes to and from the system that is known as churn. Preserving connectivity of the overlay network (i.e., the reachability of every pair of nodes) under churn without

To attract more customers, a cloud provider tends to give some discounts to a customer if he/she rents a plenty of resources. Under this situation, a group of customers who need homogeneous cloud instances with various deadlines are prone to purchasing resources in a collaborative manner, i.e., using a coalition game, to reduce purchase costs. It is essential to design a mechanism that enables all

Quality of service (QoS) provision has become an important aspect of high-performance computing interconnection networks. Proof of that is the inclusion of mechanisms targeted to the provision of QoS by the main interconnection technologies such as Gigabit Ethernet, Infiniband (IB) and Omni-Path (OPA). A key component of QoS provision is the output scheduling algorithm, which determines when a packet

Graph-tensor learning operations extend tensor operations by taking the graph structure into account, which have been applied to diverse domains such as image processing and machine learning. However, the running time of graph-tensor operations increases rapidly with the number of nodes and the dimension of data on nodes, making them impractical for real-time applications. In this paper, we propose

Machine learning over distributed data stored by many clients has important applications in use cases where data privacy is a key concern or central data storage is not an option. Recently, federated learning was proposed to solve this problem. The assumption is that the data itself is not collected centrally. In a master–worker architecture, the workers perform machine learning over their own data

Communication in data centers often involves many parallel flows that all share the same performance goal (e.g. to minimize the average completion time). A useful abstraction, coflow, is proposed to express the communication requirements of prevalent data parallel paradigms such as MapReduce and Spark. The multiple coflow routing and scheduling problem makes it challenging to derive a good theoretical

Most existing graph frameworks for GPUs adopt a vertex-centric computing model where vertex to thread mapping is applied. When run with irregular graphs, we observe significant load imbalance within SIMD-groups using vertex to thread mapping. Uneven work distribution within SIMD-groups leads to low utilization of SIMD units and inefficient use of memory bandwidth. We introduce Graph-Waving (GW) architecture

Collaborative Deep Learning (CDL) utilizes the strong feature learning capability of neural network and the model fitting robustness to solve the problem that the performance of Recommender System drops dramatically when the data is sparse. However, it makes the model training become difficult to maintain when Recommender System faces a large amount of data, and a variety of unpredictable problems

Nearest neighbors search is a core operation found in several online multimedia services. These services have to handle very large databases, while, at the same time, they must minimize the query response times observed by users. This is specially complex because those services deal with fluctuating query workloads (rates). Consequently, they must adapt at run-time to minimize the response times as

The development of cloud-assisted electronic health system effectively addresses the drawbacks of traditional medical management system. However, some challenging problems such as security and privacy in data storage and sharing cannot be ignored. First, it is difficult to ensure the integrity of electronic health records (EHRs) during the data outsourcing process. Second, it is difficult to guarantee

In this paper, we introduce a new I/O characteristic discovery methodology for performance optimizations on object-based storage systems. Different from traditional methods that select limited access attributes or heavily reply on domain knowledge about applications’ I/O behaviors, our method enables capturing data-access features as many as possible to eliminate human bias. It utilizes a machine-learning